Data Science Interview Questions and Answers – Latest

1. Best Data Science Interview Questions and Answers

As we have already discussed so many Data Science Interview Questions and Answers in our previous blogs. So, in this blog, we will be going to provide you next 30 frequently asked Data Science Interview Questions and Answers being shared by industry experts. Also, these Data Science Interview Questions and Answers will help for a data scientist, data analytics interview, data architect interview, r data science interview. Moreover, questions are totally based on R current topics. These frequently asked data science interview questions and answers will help you in cracking interviews for data scientist position.

So, let’s explore Data Science Interview Questions and Answers.

Data Science Interview Questions and Answers

Data Science Interview Questions and Answers

2. Top 30 Frequently asked Data Science Interview Questions & Answers

There are the frequently asked Data Science interview questions and Answers which you will encounter in most of the Data Science interviews.
Q.1. What is the difference between rnorm and runif functions?
rnorm function-
Basically, it generates “n” normal random numbers. That is totally based on the mean and standard deviation arguments passed to the function.
Syntax of rnorm function

rnorm(n, mean = , sd = )
runif function-

Basically, it generates “n” unform random numbers in the interval. That is of minimum and maximum values passed to the function.
Syntax of runif function

runif(n, min = , max = )

Q.2. Write the syntax to set the path for a current working directory in R environment?
Setwd(“dir_path”)
Different syntax can be asked in R Data science interview questions.
Q.3. Mention what does not ‘R’ language do?
Though R programming can easily connect to DBMS is not a database.
• Since R is open source language but still it does not consist of any graphical user interface.
• Also, it easily connects to Excel/Microsoft Office easily. Although, it does not provide any spreadsheet view of data.
Q.4. How many sorting algorithms are available?
Basically, there are 5 types of sorting algorithms are used which are:-

30 Frequently asked Data Science Interview Questions

Data Science Interview Questions  and Answers – Sorting Algorithms

Q.5. How can you produce co-relations and covariances?
Since Co-relations is produced by cor() and covariances are produced by cov() function, we need to use them.
Q.6. Describe strsplit() in R string manipulation?
Keywords
Character
Usage
strsplit(x, split, fixed = FALSE, perl = FALSE, useBytes = FALSE)
Arguments
a. x
It is a character vector, each element of which is to be split.
b. split
Basically, it is a character vector containing regular expression(s). That is used for splitting.
c. fixed
Since it is TRUE then it will match split exactly.
d. perl
Should Perl-compatible regexps be used?
e. useBytes
It is TRUE then the matching will do byte-by-byte rather than character-by-character, and inputs with marked encodings are not converted.
When asked to describe anything in data science interview questions, you mention more than one point (2-5) about the topic. In addition, you can follow a link for frequently asked R interview questions.
Q.7. How many tools for debugging present in R?
Basically, there are five tools present for debugging in R.

Data Science Interview Questions: Debugging tools

Data Science Interview Questions and Answers: Debugging tools

Read more about debugging and their tools.
Q.8. Explain how to Merge the files into a single dataframe?
At last, we have to iterate the list of files in the current working directory. Also, we need to put them together to form a data frame. Moreover, when the script encounters the first file in the file_list, then it creates the main data frame to merge everything into. This is done using the !exists conditional:

  • If their dataset exists, then a temp_dataset called temporary data frame will be created and added to the dataset. Moreover, we have to delete temporary data frame. That is been removed when we’re done with it using the rm(temp_dataset) command.
  • If dataset doesn’t exist (!exists is true), then we have to create it.

Learn more about merging files into a dataframe in detail.

3. Data Science Interview Questions And Answers for Beginners

Below are the basic Data Science Interview Questions and Answers for freshers, which can be asked in the Data Science interview of freshers or less experienced candidate. However, experienced people can also refer these question for advanced knowledge.
Q.9. Which data object in R is used to store and process categorical data?
It seems like the Factor data objects in R are used to store and process categorical data in R
Learn more about R Factor functions
Q.10. Explain how to name the list elements in R?
We have to create a list containing a vector, a matrix, and a list:
list_data <- list(c(“Feb” ,”Mar”, “Apr”), matrix(c(3,9,5,1,-2,8), nrow = 2),  list(“green” ,12.3))
Give names to the elements in the list:
names(list_data) <- c(“1st Quarter”, “A_Matrix“, “A Inner list”)
Show the list:
print(list_data)
When we execute the above code, it produces the following:
Result-
$1 st  Quarter’[1] “Feb”, “Mar”, “Apr”
$A Matrix         [,1] [,2] [,3] [1,]       3     5   -2
[2,]       9     1    8
$A_Inner_list
$A_Inner_list [[1]] [1] “Green”
$A_Inner_list [[2]] [1] “12.3”
Read more about list elements in detail.
Q.11. Explain more functions in brief in R?

  • read.spss Function – read.spss
    What it does – Reads spss data file
    For Example- spss(“myfile”)
  • read.xport Function – read.xport
    What it does – Reads SAS export file
    For Example- export(“myfile”)
  • read.dta Function – read.dta
    What it does – Reads stata binary file
    For Example – read.dta(“myfile”)

Q.12. Explain how to create a function in arguments using apply() in R?
What if we want to be able to find how many data points (n) are in each column of m?
We are using columns, MARGIN = 2, thus, we can use length function to do this:
apply(my.matrx, 2, length)
There isn’t a function in R to find n-1 for each column. So if we want to, we have to create our own Function. Since the function is simple, you can create it right inside the arguments for applying. In the arguments, I created a function that returns length – 1.
apply(my.matrx, 2, function (x) length(x)-1)
The function returned a vector of n-1 for each column.
Read more about this in detail.
Q.13. Explain for loop control statement in R?
A loop is a sequence of instructions that is repeated until a certain condition is been reached. for, while and repeat, with the additional clauses break and next are used to construct loops.
For Example-
It is executed a known number of times for a block is been contained within curly braces.

x = c(1,2,3,4,5)
for(i in 1:5){
print(x[i])
}
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
Read more about loop in detail.
Q.14. Describe nchar() in R string manipulation?
To find out if elements of a character vector are non-empty strings or not then nzchar is the fastest way.
Keywords
character
Usage
nchar(x, type = “chars”, allowNA = FALSE, keepNA = NA)
nzchar(x, keepNA = FALSE)
Arguments
a. x
Basically, a character vector or a vector will be restricted to a character vector. Giving a factor is an error.
b. type
character string: partial matching to one of c(“bytes”, “chars”, “width”).
c. allowNA
Should NA will return for invalid multibyte strings or “bytes”-encoded strings
d. keepNA
The default for nchar(), NA, means to use keepNA = TRUE unless type is “width”. Used to be hardcoded to FALSE in R versions ≤ 3.2.0.

4. Data Science Interview Questions and Answers for Experienced

These are the advanced Data Science Interview Questions and Answers for Experienced candidates. However, freshers can also refer these data science interview questions and Answers for advanced knowledge.
Q.15. Describe substr() in R string manipulation?
Generally, Substrings of a Character Vector Extractor replaces substrings in a character vector.
Keywords
Character
Usage
substr(x, start, stop)
substring(text, first, last = 1000000L)
substr(x, start, stop) <- value
substring(text, first, last = 1000000L) <- value
Arguments
a. x, text
a character vector.
b. start, first
integer. The first element that should be replace
c. stop, last
integer. The last element that should be replaced.
d. value
a character vector, recycled if necessary.
Read more about this function in detail.
Q.16.How many data structures does R language have?
It has two data structures namely:
Homogeneous data structures
It contains the same type of objects – Vector, Matrix, and Array.
Heterogeneous data structures
It contains a different type of objects – Data frames and lists.
Learn more about R data structures.
Q.17. What will be the output of log (-5.8) when executed on R console?
As we execute this value on R console will display a warning sign. That is NaN (Not a Number) will be produced. As because it is not possible to take the log of a negative number.
Along with this, you can also learn the difference between R vs SAS vs SPSS. Also, this is a hot topic.
Q.18. What is factor variable in R language?
Factor variables are categorical variables that hold either string or numeric values. Factor variables are used in various types of graphics and particularly for statistical modeling where the correct number of degrees of freedom is assigned to them.
Learn more about factors in detail.
Q.19 How R commands are written?
By using # at the starting of the line of code like #division commands are written.
Q.20 Explain the significance of transpose in R language
Transpose t () is the easiest method for reshaping the data in R before analysis.
Learn more about transpose in detail.
Q.21. How can you add datasets in R?
We use rbind() function to add datasets provided columns in the datasets should be same.
Q.22.  What are the data types in R on which binary operators can be applied?
Scalars, Matrices, and Vectors are R data types.
Read more about data types in detail.
Q.23. How can you debug and test R programming code?
R code can be tested using Hadley’s test package.
Learn more about debugging and test R programming in detail.
Q.24. Describe regex() in R string manipulation?
Create a regex. Then creates a regex object. Afterwards, build regular expressions in a human-readable way.
Usage
regex(…)
perl_regex(…)
## S3 method for class ‘perl_regex’:
format(x, …)
Arguments
a. ….
Passed to paste0.
b. x
A regex.
Learn more about regex() in detail.

5. Advanced Interview Questions for Data Science

These are the advanced Data Science Interview Questions and Answers where you have a chance to prove you knowledge in data science and grab the job with a good salary offer.
Q.25. Which function in R language is used to find out whether the means of 2 groups are equal to each other or not?
t.tests()
Learn more about R t-tests.
Q.26. Describe sprintf() in R string manipulation?
Used for C-style String Formatting Commands.
Keywords
print, character
Usage
sprintf(fmt, …)
gettextf(fmt, …, domain = NULL)
Arguments
a. fmt
It is a type of a character vector of format strings. In addition, it’s each of up to 8192 bytes.
b. .…
values will passes into fmt.
c. domain
see gettext.
Read more this function in detail.
Q.27. Describe grep() in R string manipulation?
Basically we use it for pattern matching and replacement.
For Example- grep, grepl, regexpr, gregexpr and regexec search for matches to argument pattern within each element of a character vector. Also, we use sub and gsub. Moreover, it helps in performing replacement of the first and all matches.
Keywords
Utilities, character
Usage
grep(pattern, x, ignore.case = FALSE, perl = FALSE, value = FALSE,
fixed = FALSE, useBytes = FALSE, invert = FALSE)
grepl(pattern, x, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
sub(pattern, replacement, x, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
gsub(pattern, replacement, x, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
regexpr(pattern, text, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
gregexpr(pattern, text, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
regexec(pattern, text, ignore.case = FALSE, perl = FALSE,
fixed = FALSE, useBytes = FALSE)
Arguments
a. pattern
It is a type of Character string. Also, contains a regular expression that should match in the given character vector.
b. x, text
It is an object. But, it is restricted as character.
c. ignore.case
If FALSE-
then pattern matching is case sensitive
if TRUE-
the case will ignore during matching.
d. perl
Should Perl-compatible regexps be used
e. value
A vector containing the indices of the matches determined by grep will return then it is FALSE. A vector containing the matching elements themselves will return then it is TRUE.
f. fixed
If TRUE then a pattern is a string that should match as is and it will Override all conflicting arguments.
g. useBytes
In this particular case, we use it as a condition. That is :
If TRUE-
matching will do byte-by-byte rather than character-by-character.
h. invert
Basically, in this particular case, we use it as a condition. That is :
If TRUE-
As a result, it will return values for elements that do not match.
i. replacement
Basically, it is a replacement for the matched pattern in sub and gsub.
Q.28. Explain how to write a table to a file?
First of all, write.table() function is been used to work. it is same as read.table()and which writes a data frame instead of reading one. In addition, while writing a matrix to a file, we need not have to know row or column names like:
<- write.table(xc,”xcnew” ,row.names=F,col.names=F)
Q.29. Explain how to operate on file and directory?
Basically, we have to merge all files in a directory using R into a single data frame.
Set the directory
setwd(“target_dir/”)
Getting a list of files in a directory
file_list <- list.files()
If we want to list the files in a different directory, finally specify the path to list.files.
For example:
If we want the files in the folder C:/foo/, consequently we can use the following code:
file_list <- list.files(“C:/foo/”)
Q.30. What is a class in R?
Generally, we use Class as the outline or design of the object. Also, encapsulates the data members along with the function
In addition, you can follow below link for other interview questions which are basically for freshers as well as experienced people.
R Interview Questions and Answers for Freshers & Experienced
So, this was all on Data Science Interview Questions and Answers.

6. Conclusion

As a result, we have discussed each and every type of frequently asked Data Science interview Questions and Answers in this respective blog. Also, will surely help you to prepare yourself for the interview as well. Moreover, it contains every type of questions which is great for the interview as well knowledge purpose also. Furthermore, if you have any query on Data Science Interview Questions and Answers, feel free to ask in a comment section.
In addition, you can follow a link to top books on R. Also, a link to books on Data science

Reference for Data Science

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